Results 131 to 140 of about 1,572,835 (188)

Convolutional Neural Networks for Raw Signal Classification in CNC Turning Process Monitoring. [PDF]

Sensors (Basel)
Stathatos E, Tzimas E, Benardos P, Vosniakos GC.   +3 more
europepmc   +1 more source

An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection. [PDF]

Sci Rep
Kantipudi MVVP, Kumar NSP, Aluvalu R, Selvarajan S, Kotecha K.   +4 more
europepmc   +1 more source

Optimizing RNNs for EMG Signal Classification: A Novel Strategy Using Grey Wolf Optimization. [PDF]

Bioengineering (Basel)
Aviles M, Alvarez-Alvarado JM, Robles-Ocampo JB, Sevilla-Camacho PY, Rodríguez-Reséndiz J.   +4 more
europepmc   +1 more source

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NONLINEAR SIGNAL CLASSIFICATION

International Journal of Bifurcation and Chaos, 2002
In this contribution, we show that the incorporation of nonlinear dynamical measures into a multivariate discrimination provides a signal classification system that is robust to additive noise. The signal library was composed of nine groups of signals.
Rapp, P. E., Watanabe, T. A. A., Faure, P., Cellucci, C. J.   +3 more
openaire   +2 more sources

EEG signal classification

2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2005
The article describes the classification of simple movements using a system based on Hidden Markov Models (HMM). Brisk extensions and flexions of the index finger, and movements of the proximal arm (shoulder) and distal arm (finger) were classified using scalp EEG signals. The aim of our study was to develop a system for the classification of movements
J. Stastny, P. Sovka, A. Stancak
openaire   +1 more source

Signal averaging and shape classification

Images of the Twenty-First Century. Proceedings of the Annual International Engineering in Medicine and Biology Society, 2003
Signal averaging in equal-shape and equal-width signal classes is discussed. The aim is to compare two similarity criteria associated with the same clustering algorithm; one derived from the distribution function method and the other derived from correlation.
Rix, Hervé, Thierry, Eric, Caminal, Pere, Jané, Raimon   +3 more
openaire   +1 more source

Pattern Classification of Phylogeny Signals

Statistical Applications in Genetics and Molecular Biology, 2008
In this paper we propose the minimum entropy clustering (MEC) method for clustering genes based on their phylogenetic signals. This entropy based method will cluster two genes together when their concatenation can decrease the entropy. An integral feature of MEC is that it chooses the number of clusters automatically, which is a major advantage over ...
Xiaofei, Shi, Hong, Gu, Chris, Field
openaire   +2 more sources

Automatic classification of electromyographic signals

Electroencephalography and Clinical Neurophysiology, 1983
The results of the application of classification methods to electromyograph signals of weak contractions in normal and myopathic subjects are described. Methods of pattern recognition, previously presented, allow the selection of representative motor unit action potentials.
J L, Coatrieux, P, Toulouse, B, Rouvrais, R, Le Bars   +3 more
openaire   +2 more sources